Methods and apparatus to monitor online activity

ABSTRACT

An example method to monitor online activity involves comparing first uniform resource locators collected from a first client device of a known panelist with second uniform resource locators collected from second client devices associated with different users; and determining which of the second uniform resource locators correspond to online activity of the known panelist based on ones of the first uniform resource locators matching at least portions of ones of the second uniform resource locators

FIELD OF THE DISCLOSURE

The present disclosure relates generally to audience measurement and,more particularly, to methods and apparatus to monitor online activity.

BACKGROUND

To track Internet usage, media measurement entities sometimes recruitpanel members that consent to having their Internet activity monitored.Some monitoring is done by installing a meter on a panelist's computer.The meter logs visits to websites and/or other Internet activity inInternet activity logs, and reports collected Internet activity logs toa data center from time to time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example system constructed in accordance with theteachings of this disclosure to monitor online activities of panelmembers on non-metered secondary devices.

FIG. 2 depicts an example internet protocol (IP) address registrationprocess to associate public IP addresses used by non-metered secondarydevices with corresponding panel member identifiers.

FIG. 3 depicts an example registration instruction web page useable toregister panel members at non-metered secondary devices, and to collectpublic IP addresses used by the non-metered secondary devices.

FIG. 4 depicts an example registration confirmation website confirmingthat a public IP address used by a non-metered secondary device has beenregistered.

FIG. 5 illustrates an example manner of determining non-metered onlineactivities attributable to panel members based on comparative analysesof uniform resource locators (URL's) known to have been historicallyfrequented by the panel members and URL's collected from non-meteredsecondary devices.

FIG. 6 is a flowchart which is representative of example machinereadable instructions that may be executed to monitor online activitiesof panel members at non-metered secondary devices.

FIG. 7 is a flowchart which is representative of example machinereadable instructions that may be executed to perform comparativeanalyses between metered online activities of a panelist and non-meteredonline activities to identify portions of the non-metered onlineactivities corresponding to the panelist.

FIG. 8 is an example processor system that may be used to execute theexample instructions of FIGS. 6 and/or 7 to implement example apparatusand systems disclosed herein.

DETAILED DESCRIPTION

Examples methods, apparatus, and articles of manufacture disclosedherein enable monitoring online activity. Disclosed examples enablemonitoring online activity (e.g., Internet activity) over prolongeddurations (e.g., weeks, months, etc.) and/or across multiple devices atwhich Internet usage meters are not installable and/or at which cookiesto identify users cannot consistently be relied upon. Example devicesinclude, for example, computers (stationary or mobile), smarttelevisions, gaming consoles, Internet appliances, mobile devices suchas mobile phones, smart phones, tablet devices (e.g., an Apple® iPad®tablet device), multi-media phones, etc. Examples disclosed herein maybe used to provide media providers (e.g., movie producers/distributors,music producers/distributors, television networks, online informationpublishers, website owners, ad publishers, streaming media providers,etc.) with online activity behaviors, online activity trends, mediaexposure information, etc. to enable such media providers to make moreinformed decisions about what types of media to provide, where to spendadvertising dollars, how to distribute media/advertisements, etc. Suchexamples are beneficial to media networks, website owners, marketers,product manufacturers, service companies, advertisers, and/or any otherindividual or entity that distributes or publishes media over theInternet. In addition, consumers benefit from more appealing mediaofferings, more efficient advertising (e.g., ads more relevant to theconsumer interests), and/or more appealing programming sponsored byadvertisers.

Prior systems for tracking Internet usage sometimes install meters(e.g., hardware and/or software meters) on home computers (e.g.,panelist home computers) of consumers (e.g., panel members) that consentto having their Internet activity monitored. As panel members engage intheir online activities at their home computers, the installed metersgenerate online activity logs to store information collected aboutaccessed websites and/or other online activities. The meters then sendthe Internet activity logs to data collection centers for storage and/oranalysis. While having meters installed at home computers facilitatesmonitoring Internet activities of panelists on their home computers,such meter-based techniques are often not useable to monitor theactivities of panelists on secondary devices within or outside the home(e.g., work computers, library computers, school computers, tablets,mobile phones, mobile devices, etc.). For example, work computers andcomputers located at other institutions are often governed bycorporate-wide IT (information technology) policies that prohibitinstalling unauthorized third-party hardware and/or software such asInternet usage meters. In some instances, anti-virus software oncorporate computers will seek and uninstall third-party software such asInternet usage meters. In addition, meters may often times not bereadily installable on closed systems such as mobile devices and tabletcomputers. For example, tablets, mobile phones, and/or other mobiledevices that use services from telecommunication service carriers (e.g.,wireless network service providers) are often governed by IT policies,software restrictions, hardware restrictions, etc. of carriers and/ordevice manufacturers. As such, Internet activities of panelists fromwork computers or closed system devices cannot be tracked usinginstalled meters.

Examples disclosed herein enable monitoring online activities ofpanelists when they access the Internet using non-metered secondarydevices (e.g., work computers, library computers, school computers,tablets, mobile phones, mobile devices, etc.). Disclosed examples tomonitor Internet activities of panelists on non-metered secondarydevices involves uniform resource locator (URL) analyses, in which apanelist's frequently visited websites from a home metered computer arecompared to frequently visited websites of unknown users on non-meteredsecondary devices to determine online activity from the non-meteredsecondary devices that corresponds to the panelist. In disclosedexamples, a URL usage profile is created for each home panelist based ontheir monitored Internet activities from their metered home computer. Inaddition, browsing activities of non-metered secondary devices (e.g.,work computers) are monitored to generate non-metered online activitylogs based on the browsing activities at the non-metered secondarydevices. The visited websites for non-metered online activity logs arethen compared to visited websites collected in the metered onlineactivity logs of different home panelists. High-probability matchesbetween the non-metered online activity logs and the metered onlineactivity logs are then identified. For each high-probability match,cookie ID(s) of one or more non-metered secondary devices are mapped tothe matching home panelist in a mapping table. In some examples, thematching process between website visits from non-metered secondarydevices and metered panelist computers is done from time to time tore-map any new secondary device cookie IDs resulting from users orbrowsers deleting cookies (e.g., when a user logs off or turns of acomputer at the end of a work day or at any other time) and new cookiesbeing set. In this manner, the mappings of secondary device cookies tohome panelists can be dynamically updated to follow the home panelistseven when secondary device cookies change from time to time.

To collect online activities of persons at secondary devices (e.g., workcomputers located in enterprise environments in which Internet usagemeters are not installable) disclosed examples use media taggingtechniques. In disclosed examples, media tagging facilitates monitoringusers' visits to websites that have been tagged with beacon code for useby an audience metering entity (AME) in monitoring Internet activity.Online media tagging techniques involve inserting beacon instructions ina webpage that are downloaded along with (and/or refer to a link in) thecontents of the webpage to a device browser. The browser then executesthe beacon instructions when the webpage is constructed at the browser.The instructions cause the browser to send a beacon request to a datacollection server. The beacon request includes user identifierinformation (e.g., a cookie or other unique identifier) identifying theparticular browser or device sending the beacon request and the URL ofthe website causing the beacon request. In this manner, the beaconrequest causes the data collection server to log a website visit bystoring the received user identifier information in association with theURL of the particular website that caused the beacon request to be sent.For each secondary device cookie ID, a corresponding non-metered onlineactivity log is generated at the data collection server. In this manner,using media tagging techniques, a data collection server logs websitevisits (e.g., logs URL's of websites) in association with respectivesecondary device cookie IDs. Each secondary device cookie ID representsa particular unknown user at a secondary device. In some examples, morethan one secondary device cookie ID represents the same unknown userwhen a secondary device of the unknown user frequently deletes itscookie ID, resulting in the AME setting frequently setting a new cookieID at the secondary device.

FIG. 1 depicts an example system 100 to monitor online activities ofpanel audience members (e.g., a panel audience member 102) on one ormore non-metered secondary devices 104 a-c. In the illustrated exampleof FIG. 1, online activity of a panel audience member 102 (i.e., apanelist 102) is monitored at a panelist computer 106 and at one or morenon-metered secondary devices 104 a-c. The panel member 102 of theillustrated example is a person recruited by an audience measuremententity (AME) 108 (e.g., The Nielsen Company) to become part of anaudience measurement panel and participate in audience measurementmarket research. To participate in the audience measurement panel, thepanel member 102 provides personal information (e.g., demographicinformation) to be stored by the AME 108 in association with information(e.g., a panelist ID) identifying the panel member 102. In addition, thepanel member 102 agrees to allow the AME 108 to monitor her/his onlineactivities for the duration of the agreement.

In the illustrated example, the panel member 102 resides in a panelisthousehold 110 at which the panelist computer 106 is located. When thepanel member 102 joins the audience measurement market research programimplemented by the AME 108, the AME 108 installs an example meter 112 onthe panelist computer 106. The meter 112 of the illustrated examplestores a panelist identifier (ID) 114 and collects online activity 116.The online activity 116 of the illustrated example includes information(e.g., site ID's) indicative of websites visited using the panelistcomputer 106. From time-to-time, or continuously, the meter 112 reportsor sends the panelist ID 114 and the online activity 116 to the AME 108.The meter 112 of the illustrated example may be a software meterinstalled in the panelist computer 106, a hardware meter connected to orinstalled in the panelist computer 106, or a combination of software andhardware operating jointly to implement the meter 112.

In the illustrated example, the AME 108 can use the informationcollected by the meter 112 to determine the online activities of thepanel member 102 based on the panelist ID 114 stored at the meter 112.However, when the panel member 102 uses a non-metered secondary device(e.g., one or more of the non-metered secondary devices 104 a-c) thatdoes not have a meter (e.g., the meter 112), the online activity of thepanel member 102 is not readily collectible using prior techniquesbecause such non-metered secondary device(s) do(es) not store thepanelist ID 114. Therefore, when using non-metered secondary devices toaccess the Internet, identities of panel members (e.g., the panel member102) are not available to the AME 108 and, thus, the online activitiesof such panel members on non-metered secondary devices cannot be trackedusing prior techniques. Examples disclosed herein enable the AME 108 toidentify panel members (e.g., the panel member 102) when the panelmembers access the Internet using non-metered secondary devices (e.g.,one or more of the non-metered secondary devices 104 a-c).

In the illustrated example of FIG. 1, the one or more non-meteredsecondary devices 104 a-c is/are used by the panel member 102 in a workenvironment 118 (e.g., a corporate office or other work area). Others ofthe non-metered secondary devices 104 a-c not used by the panel member102 are used by other persons in the work environment 118 which may ormay not be panel members of an audience panel managed by the AME 108.The non-metered secondary devices 104 a-c may be computers, tabletdevices, mobile phones, and/or any other device capable of accessing theInternet (e.g., a smart television, a gaming console, an Internetappliances, etc.). In illustrated examples disclosed herein, thenon-metered secondary devices 104 a-c are referred to as non-metered,because they do not have a meter, such as the meter 112 installed at thepanelist household 110. However, example techniques disclosed hereinenable monitoring Internet activity on the non-metered secondary device104 a-c without a meter (e.g., the meter 112) installed at thosesecondary devices 104 a-c. Thus, the term “non-metered” refers to nothaving a meter (e.g., not having the meter 112), and the term“secondary” refers to the devices 104 a-c being secondary devicesrelative to the panelist computer 106, which is the primary device onwhich the AME 108 collects online activity via the meter 112.

Although the panelist 102 has been described as using one or more of thenon-metered secondary devices 104 a-c, the AME 108 is unaware of exactlywhich of the non-metered secondary devices 104 a-c is used by thepanelist 102. For example, the work environment 118 may be governed by astrict corporate and/or information technology (IT) policy thatprohibits the AME 108 from installing any type of software and/orhardware meters on the non-metered secondary devices 104 a-c. In theillustrated example, to determine the online activity of the panelmember 102 when at the work environment 118, the AME 108 performscomparative analyses between URL's visited by the panelist 102 via thepanelist computer 106 and URL's visited via the non-metered secondaryclient devices 104 a-c at the work environment 118. The AME 108 thendetermines that online activity of the panelist 102 from the workenvironment 118 corresponds to online activity logs collected from thework environment 118 having sufficiently similar URL's to URL's visitedvia the panelist computer 106. In some examples, the panelist 102 usesonly one of the non-metered secondary devices 104 a-c (e.g., thepanelist 102 uses only a desktop computer at work), and other people inthe work environment use others of the non-metered secondary devices 104a-c. In other examples, the panelist 102 uses more than one of thenon-metered secondary devices 104 a-c. For example, the panelist 102 mayuse an office computer, a laboratory computer, a tablet device connectedto a local wireless local area network (WLAN), and/or a smartphoneconnected to the WLAN. Although examples disclosed herein are describedin connection with the work environment 118, disclosed examples may besimilarly used to monitor non-metered online activities in any othermonitored environment.

In the illustrated example of FIG. 1, the AME 108 is provided with ametered/active collection database 122 and a non-metered/passivecollection database 124. In some examples, the non-metered/passivecollection database 124 may be implemented using the Nielsen SiteCensusdatabase and service provided by The Nielsen Company. The metered/activecollection database 122 of the illustrated example collects meteredonline activity logs 126 from the meter 112 and from similar oridentical meters installed at other panelist households. In theillustrated example, metered online activity logs 126 collected by themetered/active collection database 122 include the panelist ID 114 andthe online activity 116. In the illustrated example, the metered/activecollection database 122 includes a data structure 128 to store site ID's130 from the online activity 116, and panelist IDs 132 (e.g., thepanelist ID 114). In the illustrated example, the site ID's 130 areURL's of websites visited using the panelist computer 106 and/or URL'sof media served to the panelist computer 106. The site ID's 130 arecollected using any suitable technique such as by intercepting, at themeter 112, website/media information delivered to the panelist computer106, and extracting the site ID's 130 from the website/mediainformation. Computer metering techniques to collect such informationare disclosed in U.S. Pat. No. 5,675,510, to Coffey et al., which ishereby incorporated by reference herein in its entirety.

In the illustrated example, the non-metered/passive collection database124 receives online activity information from the non-metered secondarydevices 104 a-c using beacon requests. An example beacon request 134 isshown in FIG. 1 that includes a site ID field 136, a cookie field 138, atimestamp field 140, and a source IP address field 142. In theillustrated example, the non-metered secondary devices 104 a-c generateand send beacon requests 134 to the AME 108 in response to executingsoftware or scripts embedded in web pages or in media presented on webpages served to the non-metered secondary devices 104 a-c. In examplesdisclosed herein, using beacon requests 134 to collect online activityinformation stored in the non-metered/passive collection database 124 isreferred to as a non-metered or passive collection technique, becausethe AME 108 does not have to meter to analyze the Internet traffic atthe non-metered secondary devices 104 a-c. Instead, the AME 108passively waits until tagged websites cause browsers (e.g., user agents)at the non-metered secondary devices 104 a-c to send the beacon requests134 to the AME 108. In examples disclosed herein, using the meter 112 tocollect online activity information stored in the metered/activecollection database 122 is referred to as a metered or active collectiontechnique, because the meter 112 actively collects Internet traffic atthe panelist household 110 to retrieve the online activity information(e.g., the online activity information 116 includes the site IDs 130).

In the illustrated example, web pages are tagged or encoded to includecomputer executable beacon instructions (e.g., Java, javascript, or anyother computer language or script) that are executed by web browsersthat access the web pages or media via, for example, the Internet. Suchweb pages having beacon instructions are called tagged web pages ortagged websites. Irrespective of the type of media being tracked,execution of the beacon instructions causes a web browser to send arequest (e.g., referred to herein as a beacon request such as the beaconrequest 134) to a specified server (e.g., at the AME 108). The beaconrequest 134 may be implemented as a hypertext transfer protocol (HTTP)request sent to a URL or IP address of a server of the AME 108. However,whereas a traditional HTTP request is used to identify a webpage orother resource that is being requested to be downloaded, the beaconrequest 134 includes audience measurement information (e.g., a URL inthe site ID field 136, a cookie identifier in the cookie field 138, atimestamp in the timestamp field 140, and/or an IP address in the sourceIP address field 142). The server to which the beacon request 134 isdirected is programmed to log the audience measurement data of thebeacon request 134 as an impression (e.g., a web page impression) in thenon-metered/passive collection database 124. Example techniques that maybe used to implement such beacon instructions are disclosed in Blumenau,U.S. Pat. No. 6,108,637, which is hereby incorporated herein byreference in its entirety.

In the illustrated example of FIG. 1, the AME 108 may collect some orall of the information shown in the beacon request 134 using beaconrequests from non-metered secondary devices. Formats of some beaconrequests may differ from the example format of the beacon request 134,and/or some fields shown in the beacon request 134 may be empty (e.g.,when some of the information is unavailable for sending to the AME 108,and/or when beacon instructions cause a web browser to populate lessthan all of the fields 136, 138, and 140).

In the illustrated example, the source IP address field 142 is locatedin an IP header 144 of the beacon request 134. In the illustratedexample, a router 146 of the work environment 118 serves as a gatewaybetween the non-metered secondary devices 104 a-c and the Internet. Therouter 146 stores an IP address 148 that serves as a source IP addressshared by the non-metered secondary devices 104 a-c to access theInternet. When a beacon request 134 from one of the non-meteredsecondary devices 104 a-c is processed by the router 146 before sendingthe beacon request 134 to a destination (e.g., to thenon-metered/passive collection database 124), the router 146 inserts theIP address 148 into the source IP address field 142 of the IP header144. In the illustrated example, the AME 108 uses the IP address 148 todetermine the source (e.g., the work environment 118) of the beaconrequest 14.

In the illustrated example, the IP address 148 is a public IP address. Apublic IP address is an IP address that is used to exchange information(e.g., websites, media, etc.) between devices across the Internet.Public IP addresses differ from private IP addresses in that private IPaddresses are only required to be unique within private networks (e.g.,a logical network defined by the router 146 in the work environment118), whereas public IP addresses are unique throughout the Internet.Thus, although a private IP address is unique within one privatenetwork, the same private IP address may be used within another,logically separate private network.

In examples disclosed herein, beacon requests 134 report public IPaddresses (e.g., the IP address 148 in the source IP address field 142of the beacon request 134). The uniqueness of public IP addressesthroughout the Internet increases opportunities to uniquely associatedifferent public IP addresses with corresponding individual panelmembers (e.g., the panelist 102) that access the Internet using thosepublic IP addresses. For example, the non-metered secondary devices 104a-c are located at an office of an employer having a single public IPaddress 148 shared by many employees' computers to access informationacross the public Internet. If the AME 108 collects online activity logsof URL's visited from the source IP address 148, those URL's may beattributed to any one or more of the non-metered secondary devices 104a-c. Knowing that the panel member 102 is among the multiple employeeslocated at that employer's office, the AME 108 can determine which ofthe activities in the online activity logs collected from the IP address148 correspond to activities of the panel member 102 based onsimilarities or matches between the URL's in non-metered online activitylogs (e.g., one or more non-metered online activity logs 154 a-c)collected from the IP address 148 of the employer and URL's in themetered online activity logs 126 collected from the meter 112 at thepanelist household 110. In the illustrated example, URL's fromnon-metered online activity logs 154 a-c collected from the IP address148 that do not sufficiently match URL's in the metered online activitylogs 126 collected from the meter 112 at the panelist household 110 aredeemed to correspond to activities of other employees (e.g., not thepanelist 102) in the work environment 118.

In the illustrated example, the AME 108 sets AME cookies 152 a-c (e.g.,secondary device cookie ID's) in the non-metered secondary devices 104a-c so that beacon requests 134 communicate the AME cookies 152 a-c tothe non-metered/passive collection database 124 when tagged websites arerendered at the non-metered secondary devices 104 a-c. Some or all ofthe non-metered secondary devices 104 a-c may be configured to deletecookies from time to time (e.g., when a user logs off or turns of acomputer at the end of a work day or at any other time), causing the AME108 to set new cookies in the non-metered secondary devices 104 a-cwhenever previous cookies are deleted. As such, cookies may changefrequently for one or more of the non-metered secondary devices 152 a-c.As a result of cookie changes, some collected URL's grouped underdifferent cookie identifiers at the AME 108 may actually correspond to asame one of the non-metered secondary devices 104 a-c. In theillustrated examples, receipt of a beacon request 134 at the AME 108 isan opportunity for the AME 108 to determine whether an AME cookie isalready set at the corresponding non-metered secondary device (e.g.,based on whether the cookie field 138 of the received beacon request 134includes a valid cookie identifier or a null value). If a valid AMEcookie is not set, the AME 108 can respond to the beacon request 134 bysetting an AME cookie value in the non-metered secondary device. In sucha manner, the AME 108 can set the AME cookies 152 a-c in correspondingones of the non-metered secondary devices 104 a-c in response to beaconrequests 134, when AME cookies are not set therein.

In the illustrated example, the non-metered/passive collection database124 stores non-metered online activity logs 154 a-c of URL's groupedunder corresponding ones of the cookie identifiers 152 a-c. For example,a log 154 a stores URL's including “SITE 1,” “SITE 4,” and “SITE 5”reported by beacon requests 134 in association with the cookieidentifier 152 a; a log 154 b stores URL's including “SITE 6,” “SITE 7,”and “SITE 8” reported by beacon requests 134 in association with thecookie identifier 152 b; and a log 154 c stores URL's including “SITE5,” “SITE 9,” and “SITE 10” reported by beacon requests 134 inassociation with the cookie identifier 152 c. The non-metered onlineactivity logs 154 a-c of the illustrated example are associated with thesame public IP address 148 because the non-metered secondary devices 104a-c are located at the same work environment 118 corresponding to thepublic IP address 148. In the illustrated example, thenon-metered/passive collection database 124 stores the logs 154 a-c inassociation with the IP address 148 of the work environment 118.

In the illustrated example, the non-metered/passive collection database124 includes a data structure 158 to store site ID's 160, cookies 162,and IP addresses 164 received from non-metered secondary devices (e.g.,the non-metered secondary devices 104 a-c). The data in the datastructure 158 is useful to monitor or track online activities conductedvia the non-metered secondary devices. In the illustrated example, thesite ID's 160 stored in the non-metered/passive collection database 124are URL's (e.g., the URL's “SITE 1,” “SITE 4,” “SITE 5,” “SITE 6,” “SITE7,” “SITE 8,” “SITE 5,” SITE 9,” and “SITE 10”) used to identifywebsites visited via the non-metered secondary devices 104 a-c.

In the illustrated example, the cookies 162 stored in thenon-metered/passive collection database 124 include the cookies 152 a-cset by the AME 108 in the non-metered secondary devices 104 a-c. In theillustrated example, the IP addresses 164 stored in thenon-metered/passive collection database 124 include the IP address 148of the work environment 118 and/or public IP addresses of otherenvironments and/or devices from which beacon requests are received atthe non-metered/passive collection database 124.

To associate non-metered online activity logs (e.g., one or more of thenon-metered online activity logs 154 a-c corresponding to thenon-metered secondary devices 104 a-c) with panel members (e.g., thepanel member 102), the AME 108 is provided with an example comparativeanalyzer 168, an example mapper 170, an example panelist database 172,and an example panelist activity map 174. In the illustrated example,the panelist database 172 stores panelist IDs (e.g., the panelist IDs114 and 132) in association with demographic and/or other information ofcorresponding panel members (e.g., the panel member 102). In someexamples, the demographic and/or other information is collected frompanel members during initial enrollment of the panel members in a marketresearch program and/or the collected information can be updated fromtime to time.

In the illustrated example, the comparative analyzer 168 performscomparative analyses between the site ID's 160 (e.g., including URL's ofthe non-metered online activity logs 154 a-c) collected by thenon-metered passive collection database 124 and the site ID's 130 (e.g.,URL's of the metered online activity logs 126) collected by themetered/active collection database 122. The comparative analyzer 168 ofthe illustrated example uses comparative analyses to determine whichnon-metered online activity logs (e.g., the non-metered online activitylogs 154 a-c) are attributable to activities of the panel member 102 inthe work environment 118 based on URL's in the non-metered onlineactivity logs 154 a-c that sufficiently match or are sufficientlysimilar to a sufficient quantity of URL's (e.g., equal to or greaterthan a threshold quantity of matches) in the metered online activitylogs 126 collected from the panelist household 110. In the illustratedexample, comparative analyses may include text-based comparisons ofmetered URL's to non-metered URL's, cluster analyses, hamming distanceanalyses, etc.

In the illustrated example, the mapper 170 stores panelist IDs (e.g.,the panelist ID's 114 and 132) from the panelist database 172 inassociation with corresponding cookie identifiers (e.g., one or morecorresponding one(s) of the cookie identifiers 152 a-c) in the panelistactivity map 174. In this manner, panelist ID's can be associated withonline activities (e.g., the site ID's 160) logged in thenon-metered/passive collection database 124 in connection withcorresponding cookie identifiers so that the AME 108 can determine whichof its panel members visited which URL's from non-metered secondarydevices. In addition, the AME 108 can use the mapped information in thepanelist activity map 174 to associate online activities (e.g., the siteID's 160) logged in the non-metered/passive collection database 124 thatcorrespond to the panelist 102 with demographic information of thepanelist 102 stored in the panelist database 172.

In some examples, other information in addition to IP addresses andcookies may be collected by the AME 108 from non-metered secondarydevices (e.g., the non-metered secondary devices 104 a-c) to assist inidentifying online activities of panel members (e.g., the panel member102) on such non-metered secondary devices. Such other information mayinclude, for example, device types (e.g., desktop computer, tabletdevice, mobile phone, etc.) of the non-metered secondary devices 104a-c. For example, beacon requests (e.g., the beacon request 134) may beadapted to also report device types of the non-metered secondary devices104 a-c. In such examples, the non-metered/passive collection database124 may store device types 178, and the comparative analyzer 168 mayalso use the device types 178 to perform its comparative analyses. Forexample, the non-metered/passive collection database 124 of FIGS. 1 and5 may group URL's based on hierarchical criteria with a highest-levelcriteria being IP address (e.g., the IP address 148), the next-levelcriteria being device type (e.g., the device types 178), and thenext-level criteria being cookies (e.g., the AME cookies 152 a-c).

In some examples, the comparative analyzer 168 may additionally parseURL's and search for user-identifying information in the URL's (e.g.,the site ID's 160 of FIG. 1) that may assist the comparative analyzer168 in identifying URL's visited by particular panelists (e.g., thepanelist 102). For example, some URL's may include parameters specifyingnames, usernames, email addresses, or other personal identifyinginformation. For example, a person's name may appear in the URL of theirsocial website page such as the name ‘John Doe’ in the URL‘www.socialnetwork.com/˜johndoe’. In some examples, the comparativeanalyzer 168 may use such personal identifying parameters form URL's toassociate corresponding panelists with one or more cookies (e.g., theAME cookies 152 a-c) corresponding to those URL's. In this manner, anyother online activity (other URL's) grouped under a same cookie are alsodeemed to correspond with online activity of the same panelist even ifthose other URL's do not include personal identifying information.

FIG. 2 depicts an example IP address registration process to associatethe IP address 148 of the work environment 118 of FIG. 1 with the panelmember 102. In the illustrated example, to associate the IP address 148with the panel member 102, the panel member 102 is requested to visit anexample registration website 202 via a corresponding one of thenon-metered secondary devices 104 a-c. The registration website 202 ofthe illustrated example is configured to receive the IP address 148during the registration process. In this manner, the AME 108 candetermine that at least some of the online activity reported from the IPaddress 148 (e.g., via beacon requests 134 of FIG. 1) corresponds toactivities of the panel member 102 when located in the work environment118. In the illustrated example, it is understood that some onlineactivity reported from the IP address 148 corresponds to other employeesor persons located at the work environment 118.

In the illustrated example, the registration website 202 is hosted bythe AME 108. However, it may alternatively be hosted by a third-partyservice in communication with the AME 108. In some examples, the AME 108can instruct the panel member 102 to register with the registrationwebsite 202 via a corresponding one of the non-metered secondary devices104 a-c at specified intervals (e.g., daily, weekly, monthly, or anyother suitable interval) to increase the likelihood that the AME 108 hasthe most recent public IP address 148 corresponding to the workenvironment 118 should the public IP address 148 change from time totime. Such instructions to the panel member 102 may be in the form ofemails, instant messages, calendar reminders, etc. that include ahyperlink of a URL of the registration website 202. For instances inwhich the public IP address 148 is static, the AME 108 may ask that thepanel member 102 register with the registration website 202 only once,or relatively less frequently than if the public IP address 148 were adynamic IP address. In some examples, the AME 108 may offer rewards(e.g., reward points, monetary rewards, products, services, etc.) to thepanel member 102 as an incentive to register one or more times with theregistration website 202.

In the illustrated example, to begin a registration process from acorresponding one of the non-metered secondary devices 104 a-c, thepanel member 102 selects a hyperlink to send a registration request 204(e.g., an HTTP request) to the registration website 202 from one or moreof the non-metered secondary devices 104 a-c. Alternatively, the panelmember 102 enters a URL of the registration website 202 into a webbrowser. In the illustrated examples disclosed herein, to register aparticular non-metered secondary device 104 a-c, the AME 108 instructsthe panel member 102 to visit the registration website 202 from a webbrowser of a corresponding one(s) of the non-metered secondary device104 a-c connected to a network of the work environment 118. In thismanner, the public IP address reported to the registration website 202during the registration process will be the IP address 148 of the workenvironment 118.

After, the registration website 202 receives the registration request204, the registration website 202 serves a registration instruction webpage to the one or more non-metered secondary devices 104 a-c. Anexample registration instruction web page 302 is shown in FIG. 3. In theillustrated example of FIG. 3, the registration instruction web page 302informs the panel member 102 that she/he is about to register thenon-metered secondary device 104 a-c of the panel member 102, andinstructs the panel member 102 to provide a panelist account user ID 304and password 306. When the panel member 102 selects a register button308, the registration website 202 of FIG. 2 associates the IP address148 of the work environment 118 with the panelist ID (e.g., the panelistID 114) of the panel member 102. In the illustrated example, theregistration website 202 receives the IP address 148 in an IP header(e.g., similar to the IP header 144 of FIG. 1) of the registrationrequest 204 sent by the non-metered secondary device 104 a-c to theregistration website 202.

In the illustrated example, the registration website 202 uses thepanelist user ID 304 and password 306 of FIG. 3 to retrieve the panelistID 114 from the panelist database 172 using a user ID/password andpanelist ID exchange 216. In some examples, when the panel member 102selects the registration button 308 of FIG. 3, the registration website202 sets a corresponding one of the AME cookies 152 a-c and sends theAME cookie 152 a-c to a registering one of the non-metered secondarydevices 104 a-c for storing therein. In the illustrated example, theregistration website 202 sets the AME cookies 152 a-c during theregistration process to create an initial cookie-to-panelist mapping inthe panelist activity map 174. As discussed above, some non-meteredsecondary devices delete cookies frequently. As such, the AME cookies152 a-c may change frequently. For example, with each beacon request(e.g., the beacon request 134 of FIG. 1) received from the non-meteredsecondary devices 104 a-c, the AME 108 can re-set new cookie identifierswhenever the non-metered secondary devices 104 a-c do not have an AMEcookie (e.g., a previous AME cookie was deleted or an AME cookie was notpreviously set). In this manner, at least some online activity can bereported to the AME 108 using the same AME cookie identifiers beforethey are deleted and re-set in the in the non-metered secondary devices104 a-c.

In the illustrated example, the initial cookie-to-panelist mappingcreated in the panelist activity map 174 during registration with theregistration website 202 is used to build an initial online activityhistory for the panelist 102 until the cookie is deleted. Because thecookie set during registration with the registration website 202 isknown to correspond with the panelist 102, any online activity reportedwith this cookie in beacon requests 134 (FIG. 1) from the workenvironment 118 (FIG. 1) is also known to correspond to the panelist102. If the cookie lasts for an extended duration (e.g., for a day orlonger, or any duration sufficient to establish a suitable web browsinghistory), sufficient URL's may be collected in the initial onlineactivity history to establish a base or initial understanding of webbrowsing behaviors and habits (e.g., listings of URL's, times/days ofURL visits, etc.) of the panelist 102 while at the work environment 118.The comparative analyzer 168 may then use such base or initialunderstanding of web browsing behaviors and habits of the panelist 102at the work environment 118 in addition to or instead of web browsingbehaviors and habits (e.g., listings of URL's, times/days of URL visits,etc.) observed from the online activity 116 collected by the meter 112at the panelist household 110 when analyzing non-metered online activity(e.g., the non-metered online activity logs 154 a-c of FIG. 1) todetermine which online activity reported from the work environment 110corresponds to the panelist 102.

Once the cookie set during registration is deleted from the non-meteredsecondary device (e.g., one of the non-metered secondary devices 104a-c) and/or replaced by another AME cookie, a known cookie-to-panelistassociation is lost. Thus, although beacon request 134 continue toreport online activity of the panel member 102 from the work environment118, such beacon requests 134 will include a different cookie that isunknown to the AME 108 as corresponding with the panelist 102. However,examples disclosed herein can be used to perform comparative analysesbetween metered online activity (e.g., the online activity 116 ofFIG. 1) and non-metered online activity (e.g., the non-metered onlineactivity logs 154 a-c of FIG. 1) to determine which cookies correspondto the panelist 102 after the cookie set during registration is deletedand/or replaced in a corresponding non-metered secondary device used bythe panelist 102.

In the illustrated example, the registration website 202 sends the IPaddress 148 to the mapper 170. The mapper 170 of the illustrated exampleassociates the IP address 148 with the panelist ID 114 of the panelmember 102 in the panelist activity map 174. In the illustrated example,the panelist activity map 174 stores IP addresses 222 (e.g., includingthe IP address 148) in association with corresponding panelist ID's 226(e.g., including the panelist ID 114) and corresponding cookieidentifiers 228 (e.g., one or more of the cookies 152 a-c) in a datastructure 230 to monitor online activities of panel members that usenon-metered secondary devices in different environments in which meters(e.g., the meter 112 of FIG. 1) cannot be installed and/or that usenon-metered secondary devices on which the AME 108 has decided not toinstall meters. In some examples, associating IP addresses 222 withcorresponding panelist ID's 226 is useful in connection with examplesdisclosed herein to reduce the need to install meters on every devicethat the AME 108 desires to monitor. In this manner, the AME 108 maymonitor online activities of panelists on different devices byassociating IP addresses 222 with corresponding panelist ID's 226 in thepanelist activity map 174 without incurring additional costs andcomplexities associated with installing meters on all of the monitoreddevices used by the panelists. In some examples, the registrationwebsite 202 may also receive device types 232 (e.g., similar to thedevice types 178 of FIG. 1) of registering ones of the non-meteredsecondary devices 104 a-c, and the panelist activity map 174 may storethe device types 232 in association with corresponding IP addresses 222,panelist ID's 226, and cookies 228. In the illustrated example, thedevice types 232 specify the type of device of the non-metered secondarydevices 104 a-c such as desktop computer, tablet device, mobile phone,etc.

After storing the IP address 148 in association with the panelist ID 114in the panelist activity map 174, the registration website 202 of theillustrated example serves an example registration confirmation web page402 to the non-metered secondary device 104 a-c as shown in FIG. 4. Theexample confirmation web page 402 informs the panel member 102 thatregistration is complete and that online activities of the panel member102 will be monitored on the non-metered secondary device 104 a-cregistered with the registration website 202.

FIG. 5 illustrates an example manner of determining non-metered onlineactivities attributable to the panel member 102 based on performingcomparative analyses of URL's known to have been historically frequentedby the panel member 102 and URL's collected from non-metered secondarydevices (e.g., the non-metered secondary devices 104 a-c). In theillustrated example, the panelist activity map 174 stores correspondingIP addresses 222, panelist ID's 226, cookie identifiers 228, devicetypes 232, and site ID's 504. In the illustrated example, ones of thecookie identifiers 228 and the site ID's 504 correspond to onlineactivities identified as corresponding to the panel member 102 (FIGS. 1and 2). In the illustrated example, a URL analyses results datastructure 506 shows URL's visited by the panel member 102 over athree-month time frame. The URL analyses results data structure 506 ofthe illustrated example is stored in the panelist activity map 174. Inthe illustrated example, the URL analyses results data structure 506initially includes URL's (e.g., the site ID's 130 of FIG. 1) reported bythe meter 112 at the panelist household 110 of FIG. 1. Over time as thecomparative analyzer 168 identifies URL's (e.g., corresponding ones ofthe site ID's 160 of FIG. 1) visited by the panel member 102 via one ormore non-metered secondary devices (e.g., one or more of the non-meteredsecondary devices 104 a-c of FIGS. 1 and 2), the URL analyses resultsdata structure 506 also stores those URL's visited by the panel member102 from the one or more non-metered secondary devices. In this manner,the URL analyses results data structure 506 builds a larger collectionover time of online activity habits and behaviors of the panel member102.

In the illustrated example, the non-metered/passive collection database124 is shown storing the non-metered online activity logs 154 a-c ofURL's grouped under corresponding ones of the cookie identifiers 152a-c. In the illustrated example, the non-metered online activity logs154 a-c and corresponding cookie identifiers 152 a-c are groupedtogether as being received from the same IP address 148 (e.g., IPaddress ‘XXX.YYY.ZZZ.26’) corresponding to the work environment 118 ofFIG. 1. As such, knowing that the panel member 102 is associated withthe IP address 148 based on the IP address registration process of FIG.2, it is confirmed that at least some of the non-metered online activitylogs 154 a-c correspond to online activities of the panel member 102when located in the work environment 118. In the illustrated example,the comparative analyzer 168 performs comparative analyses between URL'sin the URL analyses results data structure 506 and URL's in thenon-metered online activity logs 154 a-c to determine which of thewebsite visits identified in the non-metered online activity logs 154a-c is/are attributable to online activities of the panel member 102. Inthe illustrated example, the comparative analyzer 168 may perform anysuitable type of comparative analysis. For example, the comparativeanalyzer 168 may perform text-based comparisons between URL's in the URLanalyses results data structure 506 and URL's in the non-metered onlineactivity logs 154 a-c. Additionally or alternatively, the comparativeanalyzer 168 may perform one or more types of cluster analyses betweengroups of URL's in the URL analyses results data structure 506 and theURL's in the non-metered online activity logs 154 a-c. Additionally oralternatively, the comparative analyzer 168 may determine hammingdistances between URL's in the URL analyses results data structure 506and URL's in the non-metered online activity logs 154 a-c to identifyURL's that are sufficiently close matches.

In the illustrated example, the comparative analyzer 168 determineswhich of the non-metered online activity logs 154 a-c are attributableto the panel member 102 based on which of the non-metered onlineactivity logs 154 a-c include at least a threshold quantity of URL'sthat sufficiently match (e.g., are sufficiently similar to) URL's in theURL analyses results data structure 506. In some examples, the thresholdquantity of URL's may be based on a count threshold or a percentagethreshold. For example, a count threshold may be used to specify that atleast four (or any other number) URL's must sufficiently match betweenthe URL analyses results data structure 506 and one of the non-meteredonline activity logs 154 a-c. A percentage threshold may be used tospecify that at least, for example, thirty percent (30%) (or any otherpercentage) of the URL's in one of the non-metered online activity logs154 a-c must match URL's in the URL analyses results data structure 506.

In the illustrated example, the sufficiency of matches between URL's inthe URL analyses results data structure 506 and URL's in the non-meteredonline activity logs 154 a-c is based on the type of comparativeanalysis performed. In some examples, URL's must be similar to oneanother within a similarity threshold (e.g., a threshold specifyingmaximum non-matching characters, digits, words, etc.). For example, asimilarity threshold for a text-based comparison may require that atleast domain name portions must match between two URL's. For example,the comparative analyzer 168 may determine that a sufficient matchexists between the URL ‘www.nielsen.com/ProductPages’ and the URL‘www.nielsen.com/ClientLogin’ because at least the domain name portion‘www.nielsen.com’ matches between the two URL's. For sufficiency ofmatches using hamming distances, the comparative analyzer 168 maydetermine that a sufficient match exists between the URL‘www.nielsen.com’ and the URL ‘www1.nielsen.com’ because the hammingdistance is less than a similarity threshold hamming distance. Forexample, the similarity threshold hamming distance may be three, and thehamming distance between the URL ‘www.nielsen.com’ and the URL‘www1.nielsen.com’ is one because of the one character differencebetween ‘www’ and ‘www1.’

In some examples, the comparative analyzer 168 may use text-basedcomparisons, hamming distance techniques, and/or any other comparativetechniques in connection with cluster analyses techniques to determineURL's that are more similar to one another between one of thenon-metered online activity logs 154 a-c and the URL analyses resultsdata structure 506 than between others of the non-metered onlineactivity logs 154 a-c and the URL analyses results data structure 506.In some examples, cluster analyses may be performed using times/days ofURL accesses as criteria in a cluster analyses model. For example, thepanel member 102 may access certain websites at particular times of day,which may establish a known behavior by which online activities of thepanel member 102 may be detected in non-metered online activity (e.g.,the non-metered online activity logs 154 a-c). In such examples,timestamps in the timestamp field 140 (FIG. 1) may be used to tracktimes/days of reported URL accesses. For example, timestamps may bestored in the non-metered/passive collection database 124 in associationwith corresponding site ID's 160, cookies 162, and IP addresses 164.

In the illustrated example of FIG. 1, the comparative analyzer 168determines that the non-metered online activity log 154 c isattributable to online activity of the panel member 102 because athreshold quantity of URL's in the non-metered online activity log 154 csufficiently match URL's in the URL analyses results data structure 506.In the illustrated example, the threshold quantity of sufficientlymatching URL's include “SITE A,” “SITE B,” and “SITE C.” In theillustrated example, the comparative analyzer 168 causes the mapper 170to store the cookie identifier 152 c and corresponding URL's (e.g., siteIDs) of the non-metered online activity log 154 c in association withthe panelist ID 114 (FIGS. 1 and 2) of the panel member 102 in thepanelist activity map 174. Alternatively, the mapper 170 may store thecookie identifier 152 c in association with the panelist ID 114 in thepanelist activity map 174 without storing the corresponding URL's of thenon-metered online activity log 154 c. In this manner, less storagecapacity is used in the panelist activity map 174, and URL's forcorresponding cookie ID's can be retrieved from the non-metered/passivecollection database 124 based on the cookies 228 stored in the panelistactivity map 174.

While an example manner of implementing the comparative analyzer 168 andthe mapper 170 has been illustrated in FIGS. 1, 2, and 5, one or moreelements, processes and/or devices used to implement the comparativeanalyzer 168 and/or the mapper 170 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example comparative analyzer 168 and/or the example mapper170 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,the example comparative analyzer 168 and/or the mapper 170 could beimplemented by one or more circuit(s), programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)),etc. When any of the apparatus or system claims of this patent are readto cover a purely software and/or firmware implementation, at least oneof the example comparative analyzer 168 and/or the example mapper 170are hereby expressly defined to include a tangible computer readablestorage medium such as a memory, a DVD, a CD, a Blu-ray disc, etc.storing the software and/or firmware. Further still, the exampleanalyzer 168 and/or the example mapper 170 may include one or moreelements, processes and/or devices in addition to, or instead of, thosedescribed in connection with FIGS. 1, 2, and 5, and/or may include morethan one of any or all of the described elements, processes and devices.

Flowcharts representative of example machine readable instructions toimplement the comparative analyzer 168 and/or the mapper 170 of FIGS. 1,2, and 5 are shown in FIGS. 6 and 7. In this example, the machinereadable instructions comprise programs for execution by a processorsuch as the processor 812 shown in the example computer 800 discussedbelow in connection with FIG. 8. The program may be embodied in softwarestored on a tangible computer readable storage medium such as a CD-ROM,a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisc, or a memory associated with the processor 812, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 812 and/or embodied in firmware or dedicatedhardware. Further, although the example programs are described withreference to the flowcharts illustrated in FIGS. 6 and 7, many othermethods of implementing the example comparative analyzer 168 and/or theexample mapper 170 may alternatively be used. For example, the order ofexecution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 6 and 7 may beimplemented using coded instructions (e.g., computer readableinstructions) stored on a tangible computer readable storage medium suchas a hard disk drive, a flash memory, a read-only memory (ROM), acompact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termtangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals. Additionally or alternatively, theexample processes of FIGS. 6 and 7 may be implemented using codedinstructions (e.g., computer readable instructions) stored on anon-transitory computer readable storage medium such as a hard diskdrive, a flash memory, a read-only memory, a compact disk, a digitalversatile disk, a cache, a random-access memory and/or any other storagemedia in which information is stored for any duration (e.g., forextended time periods, permanently, brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm non-transitory computer readable storage medium is expresslydefined to include any type of computer readable storage medium and toexclude propagating signals. As used herein, when the phrase “at least”is used as the transition term in a preamble of a claim, it isopen-ended in the same manner as the term “comprising” is open ended.Thus, a claim using “at least” as the transition term in its preamblemay include elements in addition to those expressly recited in theclaim.

Turning to the example process of FIG. 6, initially the metered/activecollection database 122 (FIG. 1) collects online activity (e.g., themetered online activity logs 126 of FIG. 1) from the panelist computer106 (FIG. 1) of the known panelist 102 (block 602). For example, themetered/active collection database 122 receives the metered onlineactivity logs 126 from the meter 112 of FIG. 1. The AME 108 (FIG. 1)instructs the panelist 102 to register from a corresponding non-meteredsecondary device (e.g., one of the non-metered secondary devices 104 a-cof FIGS. 1, 2, and 5) (block 604). For example, a server or othercomputer of the AME 108 may send an email, a text message, or any othersuitable notification or message to the panelist 102 instructing thepanelist 102 to register an IP address used by a non-metered secondarydevice used by the panelist 102.

The AME 108 receives a registration request 204 (FIG. 2) to register anIP address 148 used by a non-metered secondary device 104 a-c of thepanelist 102 (block 606). For example, the AME 108 receives aregistration request 204 at the registration website 202 (FIG. 2) inresponse to the panelist 102 navigating to a URL of the registrationwebsite 202 (e.g., entering the URL in a web browser or selecting aregistration link) from a corresponding one of the non-metered secondarydevices 104 a-c. The mapper 170 (FIGS. 1 and 2) associates theregistered IP address 148 with the panelist ID 114 (FIGS. 1 and 2) ofthe panelist 102 (block 608). For example, the mapper 170 stores the IPaddress 148 in association with the panelist ID 114 in the examplepanelist activity map 174 (FIGS. 1 and 2).

The non-metered/passive collection database 124 (FIGS. 1, 2, and 5)collects online activities from the non-metered secondary devices 104a-c that share the registered IP address 148 (block 610). For example,the non-metered/passive collection database 124 collects URL's viabeacon requests 134 (FIG. 1) received from the non-metered secondarydevices 104 a-c. The non-metered/passive collection database 124 logsURL's (e.g., the site ID's 160 of FIG. 1) collected from the non-meteredsecondary devices 104 a-c into groups based on cookie identifiers (e.g.,the cookie identifiers 152 a-c of FIG. 1) received in association withthe URL's (block 612). For example, the non-metered/passive collectiondatabase 124 groups the URL's into the non-metered online activity logs154 a-c for corresponding ones of the cookies 152 a-c as shown in FIGS.1 and 5.

The comparative analyzer 168 performs a comparative analysis betweennon-metered online activity and metered online activity (block 614). Forexample, the comparative analyzer 168 compares URL's in the non-meteredonline activity logs 154 a-c (FIGS. 1 and 5) with URL's in the URLanalyses results data structure 506 (FIG. 5) and/or URL's in the meteredonline activity logs 126 (FIG. 1). The comparative analyzer 168determines portions of the non-metered online activity that correspondto online activity of the panelist 102 (block 616). For example, thecomparative analyzer 168 may determine, based on the comparativeanalysis of block 614, that the non-metered online activity log 154 ccorresponds to the panelist 102 (as shown in FIG. 5) when using anon-metered secondary device 104 a-c in the work environment 118 ofFIG. 1. The mapper 170 associates the portions of the non-metered onlineactivity identified at block 616 with the panelist 102 (block 618). Forexample, the mapper 170 may store the non-metered online activity log154 c and the AME cookie 152 c (FIG. 5) in the panelist activity map 174(FIGS. 1 and 5) in association with the panelist ID 114 of the panelist102. Alternatively, the mapper 170 may store the AME cookie 152 c in thepanelist activity map 174 in association with the panelist ID 114without storing the corresponding non-metered online activity log 154 cin the panelist activity map 174. In this manner, less storage capacityis used in the panelist activity map 174 to associate the panelist 102with non-metered online activity, and the AME cookie 152 c stored in thepanelist activity map 174 can be used to look up the correspondingnon-metered online activity log 154 c in the non-metered/passivecollection database 124 of FIGS. 1 and 5. The example process of FIG. 6then ends.

FIG. 7 is representative of example machine readable instructions thatmay be executed to perform comparative analyses between metered onlineactivities (e.g., the URL analyses results data structure 506 of FIG. 5and/or the metered online activity logs 126 of FIG. 1) of a panelist(e.g., the panelist 102) and non-metered online activities (e.g., thenon-metered online activity logs 154 a-c of FIGS. 1 and 5) to identifyportions of the non-metered online activities corresponding to thepanelist. In some examples, the example process of FIG. 7 may be used toimplement the operations of blocks 614, 616, and 618 of FIG. 6.

Initially, the comparative analyzer 168 (FIGS. 1 and 5) selects anon-metered online activity log corresponding to a cookie identifier(block 702). For example, the comparative analyzer 168 may select one ofthe non-metered online activity logs 154 a-c associated with acorresponding one of the cookie identifiers 152 a-c of FIGS. 1 and 5.The comparative analyzer 168 performs a comparative analysis betweenURL's (e.g., ones of the site ID's 130 of FIG. 1) collected from thepanelist computer 106 (FIG. 1) of the panelist 102 and URL's (e.g., onesof the site ID's 160 of FIG. 1) from the selected non-metered onlineactivity log(block 704). The comparative analysis used by thecomparative analyzer 168 at block 704 may be any comparative analysisdescribed herein and/or any other suitable comparative analysistechnique(s).

The comparative analyzer 168 determine ones of the URL's (e.g., ones ofthe site ID's 130) collected from the panelist computer 106 of thepanelist 102 that sufficiently match URL's (e.g., ones of the site ID's160 of FIG. 1) from the selected non-metered online activity log (block706). The sufficiency of matches may be determined based on anysimilarity threshold-based technique described herein and/or any othersuitable technique(s) for assessing a sufficient similarity between twoobjects to confirm that a sufficient match exists.

The comparative analyzer 168 determines whether there are a thresholdquantity of URL matches (block 708) between the URL's (e.g., ones of thesite ID's 130) collected from the panelist computer 106 and the URL's(e.g., ones of the site ID's 160 of FIG. 1) from the selectednon-metered online activity log. If the quantity of URL matches is equalto or greater than a threshold (block 708), the mapper 170 associatesall online activity in selected non-metered online activity log with thepanelist ID 114 of the panelist 102 (block 710). For example, the mapper170 may store the non-metered online activity log 154 c and the AMEcookie 152 c (FIG. 5) in the panelist activity map 174 (FIGS. 1 and 5)in association with the panelist ID 114 of the panelist 102.Alternatively, the mapper 170 may store the AME cookie 152 c in thepanelist activity map 174 in association with the panelist ID 114without storing the corresponding non-metered online activity log 154 cin the panelist activity map 174. In this manner, less storage capacityis used in the panelist activity map 174 to associate the panelist 102with non-metered online activity, and the AME cookie 152 c stored in thepanelist activity map 174 can be used to look up the non-metered onlineactivity log 154 c in the non-metered/passive collection database 124 ofFIGS. 1 and 5. In some examples, the AME 108 can use the mappedinformation in the panelist activity map 174 to associate onlineactivities (e.g., the site ID's 160) logged in the non-metered/passivecollection database 124 that correspond to the panelist 102 withdemographic information of the panelist 102 stored in the panelistdatabase 172.

After the operation of block 710, or if the quantity of URL matches isnot equal to or greater than a threshold at block 708, the comparativeanalyzer 168 determines whether there is another non-metered onlineactivity log (e.g., another one of the non-metered online activity logs154 a-c) to analyze (block 712). If there is another non-metered onlineactivity log to analyze (block 712), control returns to block 712.Otherwise, the example process of FIG. 7 ends.

FIG. 8 is a block diagram of an example processor platform 800 capableof executing the instructions of FIGS. 6 and 7 to implement thecomparative analyzer 168 and/or the mapper 170 of FIGS. 1, 2, and 5. Theprocessor platform 800 can be, for example, a server, a personalcomputer, and/or any other suitable type of computing device.

The processor platform 800 of the instant example includes a processor812. For example, the processor 812 can be implemented by one or moremicroprocessors or controllers from any desired family or manufacturer.

The processor 812 includes a local memory 813 (e.g., a cache) and is incommunication with a main memory including a volatile memory 814 and anon-volatile memory 816 via a bus 818. The volatile memory 814 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 816 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 814, 816is controlled by a memory controller.

The processor platform 800 also includes an interface circuit 820. Theinterface circuit 820 may be implemented by any type of interfacestandard, such as an Ethernet interface, a universal serial bus (USB),and/or a PCI express interface.

One or more input devices 822 are connected to the interface circuit820. The input device(s) 822 permit a user to enter data and commandsinto the processor 812. The input device(s) can be implemented by, forexample, a keyboard, a mouse, a touchscreen, a track-pad, a trackball,isopoint and/or a voice recognition system.

One or more output devices 824 are also connected to the interfacecircuit 820. The output devices 824 can be implemented, for example, bydisplay devices (e.g., a liquid crystal display, a cathode ray tubedisplay (CRT), a printer and/or speakers). The interface circuit 820,thus, typically includes a graphics driver card.

The interface circuit 820 also includes a communication device such as amodem or network interface card to facilitate exchange of data withexternal computers via a network 826 (e.g., an Ethernet connection, adigital subscriber line (DSL), a telephone line, coaxial cable, acellular telephone system, etc.).

The processor platform 800 also includes one or more mass storagedevices 828 for storing software and data. Examples of such mass storagedevices 828 include floppy disk drives, hard drive disks, compact diskdrives and digital versatile disk (DVD) drives. The mass storage device828 may implement one or more of the metered/active collection database122, the non-metered/passive collection database 124, the panelistdatabase 172, and/or the panelist activity map 174 of FIGS. 1, 2, and 5.

Coded instructions 832 to implement the example processes of FIGS. 6 and7 may be stored in the mass storage device 828, in the volatile memory814, in the non-volatile memory 816, and/or on a removable storagemedium such as a CD or DVD.

Although the above discloses example methods, apparatus, systems, andarticles of manufacture including, among other components, firmwareand/or software executed on hardware, it should be noted that suchmethods, apparatus, systems, and articles of manufacture are merelyillustrative and should not be considered as limiting. Accordingly,while the above describes example methods, apparatus, systems, andarticles of manufacture, the examples provided are not the only ways toimplement such methods, apparatus, systems, and articles of manufacture.Thus, while certain example methods, apparatus and articles ofmanufacture have been described herein, the scope of coverage of thispatent is not limited thereto. On the contrary, this patent covers allmethods, apparatus and articles of manufacture fairly falling within thescope of the claims of this patent.

What is claimed is:
 1. A method to monitor online activity, the methodcomprising: comparing first uniform resource locators collected from afirst client device of a known panelist with second uniform resourcelocators collected from second client devices associated with differentusers; and determining which of the second uniform resource locatorscorrespond to online activity of the known panelist based on ones of thefirst uniform resource locators matching at least portions of ones ofthe second uniform resource locators.
 2. A method as defined in claim 1,wherein matching at least portions of ones of the second uniformresource locators comprises matching at least domain name portions ofthe ones of the second uniform resource locators.
 3. A method as definedin claim 1, wherein the second uniform resource locators are accessedvia a same internet protocol address shared by the second client devicesto access the Internet.
 4. A method as defined in claim 3, furthercomprising instructing the known panelist to register the internetprotocol address from at least one of the second client devices with anaudience measurement entity.
 5. A method as defined in claim 3, furthercomprising collecting the second uniform resource locators inassociation with the internet protocol address.
 6. A method as definedin claim 1, further comprising storing the second uniform resourcelocators into corresponding groups based on corresponding cookieidentifiers collected in association with the second uniform resourcelocators.
 7. A method as defined in claim 1, wherein matching the atleast portions of the ones of the second uniform resource locatorscomprises matching the at least portions of the ones of the seconduniform resource locators within a similarity threshold.
 8. An apparatusto monitor online activity, the apparatus comprising: a comparativeanalyzer to compare first uniform resource locators collected from afirst client device of a known panelist with second uniform resourcelocators collected from second client devices associated with unknownusers; and a mapper to associate at least some of the second uniformresource locators as online activity of the known panelist based on onesof the first uniform resource locators matching at least portions ofones of the second uniform resource locators.
 9. An apparatus as definedin claim 8, wherein matching at least portions of ones of the seconduniform resource locators comprises matching at least domain nameportions of the ones of the second uniform resource locators.
 10. Anapparatus as defined in claim 8, wherein the second uniform resourcelocators are accessed via a same internet protocol address shared by thesecond client devices to access the Internet.
 11. An apparatus asdefined in claim 10, further comprising a data structure to store thesecond uniform resource locators in association with the internetprotocol address.
 12. An apparatus as defined in claim 8, furthercomprising a data structure to store the second uniform resourcelocators into corresponding groups based on corresponding cookieidentifiers collected in association with the second uniform resourcelocators.
 13. An apparatus as defined in claim 8, wherein matching theat least portions of the ones of the second uniform resource locatorscomprises matching the at least portions of the ones of the seconduniform resource locators within a similarity threshold.
 14. A machinereadable storage medium comprising instructions that cause a machine toat least: compare first uniform resource locators collected from a firstclient device of a known panelist with second uniform resource locatorscollected from second client devices associated with different users;and determine which of the second uniform resource locators correspondto online activity of the known panelist based on ones of the firstuniform resource locators matching at least portions of ones of thesecond uniform resource locators.
 15. A machine readable storage mediumas defined in claim 14, wherein matching at least portions of ones ofthe second uniform resource locators comprises matching at least domainname portions of the ones of the second uniform resource locators.
 16. Amachine readable storage medium as defined in claim 14, wherein thesecond uniform resource locators are accessed via a same internetprotocol address shared by the second client devices to access theInternet.
 17. A machine readable storage medium as defined in claim 16,wherein the instructions further cause the machine to instruct the knownpanelist to register the internet protocol address from at least one ofthe second client devices with an audience measurement entity.
 18. Amachine readable storage medium as defined in claim 16, wherein theinstructions further cause the machine to collect the second uniformresource locators in association with the internet protocol address. 19.A machine readable storage medium as defined in claim 14, wherein theinstructions further cause the machine to store the second uniformresource locators into corresponding groups based on correspondingcookie identifiers collected in association with the second uniformresource locators.
 20. A machine readable storage medium as defined inclaim 14, wherein matching the at least portions of the ones of thesecond uniform resource locators comprises matching the at leastportions of the ones of the second uniform resource locators within asimilarity threshold.